Human-in-the-Loop

What is this?

Human-in-the-loop (HITL) lets an agent pause mid-run to collect input, confirmation, or a choice from the user, then resume with that answer folded back into its reasoning. It's what turns an autonomous workflow into a collaborative one — the agent keeps its context, the user keeps the steering wheel.

When should I use this?

Use HITL when you need:

• Quality control — a human gate at high-stakes decision points
• Edge cases — graceful fallbacks when the agent's confidence is low
• Expert input — lean on the user for domain knowledge the model lacks
• Reliability — a more robust loop for real-world, production traffic

Two patterns for HITL in CopilotKit

CopilotKit ships two complementary ways to pause an agent turn and ask the human something. They look similar from the outside — the chat pauses, a custom component appears, the user answers, the run resumes — but they're wired differently on the backend and each has its own niche.

Pick useHumanInTheLoop when the pause is an agent-initiated decision — the model chose to ask the user — and you want the picker UI inlined into the normal tool-call flow.

Pick useInterrupt when the pause is a graph-enforced checkpoint — the code path deterministically requires a human answer — and you want langgraph.interrupt() as the server-side contract.

Pattern 1 — useHumanInTheLoop (tool-based)

The agent registers a HITL tool on the client with useHumanInTheLoop. When the LLM calls that tool, CopilotKit routes the call through your render function, which shows a custom component and calls respond with the user's answer. The agent sees the answer as the tool result and continues from there.

  useHumanInTheLoop({
    agentId: "hitl-in-chat",
    name: "book_call",
    description:
      "Ask the user to pick a time slot for a call. The picker UI presents fixed candidate slots; the user's choice is returned to the agent.",
    parameters: z.object({
      topic: z
        .string()
        .describe("What the call is about (e.g. 'Intro with sales')"),
      attendee: z
        .string()
        .describe("Who the call is with (e.g. 'Alice from Sales')"),
    }),
    render: ({ args, status, respond }: any) => (
      <TimePickerCard
        topic={args?.topic ?? "a call"}
        attendee={args?.attendee}
        slots={DEFAULT_SLOTS}
        status={status}
        onSubmit={(result) => respond?.(result)}
      />
    ),
  });

The picker UI is fed a static list of candidate slots — this is just data the demo page owns, so you can swap in real availability, a calendar API, or anything else:

const DEFAULT_SLOTS: TimeSlot[] = [
  { label: "Tomorrow 10:00 AM", iso: "2026-04-19T10:00:00-07:00" },
  { label: "Tomorrow 2:00 PM", iso: "2026-04-19T14:00:00-07:00" },
  { label: "Monday 9:00 AM", iso: "2026-04-21T09:00:00-07:00" },
  { label: "Monday 3:30 PM", iso: "2026-04-21T15:30:00-07:00" },
];

Pattern 2 — useInterrupt (graph-paused)

With LangGraph's interrupt() the pause is enforced by the graph itself: a node calls interrupt({...}), the run suspends, the client receives the payload, renders a UI, and resumes the run with the user's answer. CopilotKit's useInterrupt hook is the render contract.

See the useInterrupt deep dive for the full walkthrough, including the backend tool and render-prop wiring.

Going headless

Both patterns above ship with a render prop — CopilotKit handles the "when to show the picker" logic for you. If you want to drive interrupt resolution from a custom UI that lives anywhere in the tree (not necessarily inside a chat), see the headless interrupts guide — it shows how to compose useAgent, agent.subscribe, and copilotkit.runAgent to build your own useInterrupt equivalent.

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